Book Review of: Eckler, Rebecca. (2019). Blissfully Blended Bullshit: The Uncomfortable Truth of Blending Families. Toronto: Dundurn Press.
Bibliographic record
Abstract
Rebecca reflects on her personal experience of merging her life as a single mom with her new boyfriend, along with his two daughters, a dog, and a new baby on the way.Once the honeymoon phase has worn off, Rebecca is struck with the realities and challenges of combining lives."Sure, saying that blending families is "so, so hard," is not a very interesting description of what happens when blending, but it is so, so true.Blending families is so, so fucking hard!" (p.159).She discovers the different types of love between biological parents and their children, along with the unspoken sentiments of extended family members and friends.This book follows Rebecca's journey as she attempts to navigate the new waters of her blended family.Rebecca learns how to share her space, which had once been only hers.She must also adjust to her role as a mother with a new baby and a daughter, as a girlfriend, and as a stepmother of two girls.Rebecca learns to accept the expectations required of her, such as cooking, cleaning and caring for the household.Rebecca gets down to the nitty-gritty issues that impact her family's overall functioning, such as grocery shopping, meals, and cellphone wallpapers.But she also addresses the much more significant challenges, being different parenting styles, disagreements, money, and equity.Rebecca shares her experience with many of her own friends who are blending their own
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.253 | 0.177 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".